A robot laboratory for teaching artificial intelligence
SIGCSE '98 Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Algorithms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Non-traditional projects in the undergraduate AI course
Proceedings of the 37th SIGCSE technical symposium on Computer science education
Pedagogical possibilities for the dice game pig
Journal of Computing Sciences in Colleges
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
A contextualized project-based approach for improving student engagement and learning in AI courses
Proceedings of the 16th annual joint conference on Innovation and technology in computer science education
A contextualized project-based approach for improving student engagement and learning in AI courses
Proceedings of Second Computer Science Education Research Conference
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Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that can be closely integrated into a one-term AI course. Each project involves the development of a machine learning system in a specific application. These projects have been used in six different offerings over a three-year period at three different types of institutions. While we have presented a sample of the projects as well as limited preliminary experiences in other venues, this article presents the first assessment of our work over an extended period of three years. Results of assessment show that the projects were well received by the students. By using projects involving real-world applications we provided additional motivation for students. While illustrating core concepts, the projects introduced students to an important area in computer science, machine learning, thus motivating further study.